Resampling calibrated adjusted empirical likelihood
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Empirical‐likelihood‐based inference for parameters defined by the general estimating equations of Qin & Lawless (1994) remains an active research topic. When the sample size is small and/or the dimension of the accompanying estimating equations is high, the resulting confidence regions often have a lower than nominal coverage probability. In addition, the empirical likelihood can be hindered by an empty set problem. The adjusted empirical likelihood (AEL) tackles both problems simultaneously. However, the AEL confidence region with high‐order precision relies on accurate estimation of the required level of adjustment. This has proved difficult, particularly in over‐identified cases. In this article, we show that the general AEL is Bartlett‐correctable and propose a two‐stage procedure for constructing accurate confidence regions. A naive AEL is first employed to address the empty set problem, and it is then Bartlett‐corrected through a resampling procedure. The finite‐sample performance of the proposed method is illustrated by simulations and an example. The Canadian Journal of Statistics 43: 42–59; 2015 © 2014 Statistical Society of Canada
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it